Overview

Dataset statistics

Number of variables13
Number of observations5329
Missing cells38
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory541.4 KiB
Average record size in memory104.0 B

Variable types

Categorical1
Numeric12

Alerts

alcohol is highly overall correlated with densityHigh correlation
chlorides is highly overall correlated with density and 1 other fieldsHigh correlation
density is highly overall correlated with alcohol and 1 other fieldsHigh correlation
fixed acidity is highly overall correlated with typeHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly overall correlated with free sulfur dioxide and 1 other fieldsHigh correlation
type is highly overall correlated with chlorides and 3 other fieldsHigh correlation
volatile acidity is highly overall correlated with typeHigh correlation
citric acid has 135 (2.5%) zerosZeros

Reproduction

Analysis started2024-04-30 17:25:37.878248
Analysis finished2024-04-30 17:25:56.182318
Duration18.3 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size41.8 KiB
white
3970 
red
1359 

Length

Max length5
Median length5
Mean length4.4899606
Min length3

Characters and Unicode

Total characters23927
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwhite
2nd rowwhite
3rd rowwhite
4th rowwhite
5th rowwhite

Common Values

ValueCountFrequency (%)
white 3970
74.5%
red 1359
 
25.5%

Length

2024-05-01T02:25:56.280473image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-01T02:25:56.395573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
white 3970
74.5%
red 1359
 
25.5%

Most occurring characters

ValueCountFrequency (%)
e 5329
22.3%
w 3970
16.6%
h 3970
16.6%
i 3970
16.6%
t 3970
16.6%
r 1359
 
5.7%
d 1359
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23927
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5329
22.3%
w 3970
16.6%
h 3970
16.6%
i 3970
16.6%
t 3970
16.6%
r 1359
 
5.7%
d 1359
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 23927
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5329
22.3%
w 3970
16.6%
h 3970
16.6%
i 3970
16.6%
t 3970
16.6%
r 1359
 
5.7%
d 1359
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23927
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5329
22.3%
w 3970
16.6%
h 3970
16.6%
i 3970
16.6%
t 3970
16.6%
r 1359
 
5.7%
d 1359
 
5.7%

fixed acidity
Real number (ℝ)

HIGH CORRELATION 

Distinct106
Distinct (%)2.0%
Missing10
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean7.2165727
Minimum3.8
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2024-05-01T02:25:56.489815image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.6
Q16.4
median7
Q37.7
95-th percentile9.8
Maximum15.9
Range12.1
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.3191937
Coefficient of variation (CV)0.18280058
Kurtosis4.5932541
Mean7.2165727
Median Absolute Deviation (MAD)0.6
Skewness1.6504394
Sum38384.95
Variance1.740272
MonotonicityNot monotonic
2024-05-01T02:25:56.608920image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.8 280
 
5.3%
6.6 268
 
5.0%
6.4 246
 
4.6%
6.9 225
 
4.2%
7 223
 
4.2%
6.7 212
 
4.0%
7.2 203
 
3.8%
7.1 200
 
3.8%
6.5 197
 
3.7%
6.2 177
 
3.3%
Other values (96) 3088
57.9%
ValueCountFrequency (%)
3.8 1
 
< 0.1%
3.9 1
 
< 0.1%
4.2 2
 
< 0.1%
4.4 3
 
0.1%
4.5 1
 
< 0.1%
4.6 2
 
< 0.1%
4.7 6
 
0.1%
4.8 9
 
0.2%
4.9 6
 
0.1%
5 26
0.5%
ValueCountFrequency (%)
15.9 1
< 0.1%
15.6 2
< 0.1%
15.5 1
< 0.1%
15 1
< 0.1%
14.3 1
< 0.1%
14.2 1
< 0.1%
14 1
< 0.1%
13.8 1
< 0.1%
13.7 1
< 0.1%
13.5 1
< 0.1%

volatile acidity
Real number (ℝ)

HIGH CORRELATION 

Distinct187
Distinct (%)3.5%
Missing8
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean0.34412329
Minimum0.08
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2024-05-01T02:25:56.725849image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.16
Q10.23
median0.3
Q30.41
95-th percentile0.68
Maximum1.58
Range1.5
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.16822824
Coefficient of variation (CV)0.48886037
Kurtosis2.8648728
Mean0.34412329
Median Absolute Deviation (MAD)0.08
Skewness1.5050529
Sum1831.08
Variance0.02830074
MonotonicityNot monotonic
2024-05-01T02:25:56.835494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 232
 
4.4%
0.24 218
 
4.1%
0.26 218
 
4.1%
0.27 188
 
3.5%
0.25 186
 
3.5%
0.22 184
 
3.5%
0.2 178
 
3.3%
0.23 178
 
3.3%
0.3 168
 
3.2%
0.32 164
 
3.1%
Other values (177) 3407
63.9%
ValueCountFrequency (%)
0.08 2
 
< 0.1%
0.085 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 6
 
0.1%
0.105 4
 
0.1%
0.11 9
 
0.2%
0.115 3
 
0.1%
0.12 29
0.5%
0.125 2
 
< 0.1%
0.13 36
0.7%
ValueCountFrequency (%)
1.58 1
< 0.1%
1.33 2
< 0.1%
1.24 1
< 0.1%
1.185 1
< 0.1%
1.18 1
< 0.1%
1.13 1
< 0.1%
1.115 1
< 0.1%
1.1 1
< 0.1%
1.09 1
< 0.1%
1.07 1
< 0.1%

citric acid
Real number (ℝ)

ZEROS 

Distinct89
Distinct (%)1.7%
Missing3
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.31873827
Minimum0
Maximum1.66
Zeros135
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2024-05-01T02:25:56.953137image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04
Q10.24
median0.31
Q30.4
95-th percentile0.56
Maximum1.66
Range1.66
Interquartile range (IQR)0.16

Descriptive statistics

Standard deviation0.14711633
Coefficient of variation (CV)0.46155843
Kurtosis2.578799
Mean0.31873827
Median Absolute Deviation (MAD)0.07
Skewness0.48438568
Sum1697.6
Variance0.021643215
MonotonicityNot monotonic
2024-05-01T02:25:57.061573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 264
 
5.0%
0.32 240
 
4.5%
0.28 236
 
4.4%
0.49 232
 
4.4%
0.34 204
 
3.8%
0.26 204
 
3.8%
0.29 198
 
3.7%
0.31 187
 
3.5%
0.24 184
 
3.5%
0.27 179
 
3.4%
Other values (79) 3198
60.0%
ValueCountFrequency (%)
0 135
2.5%
0.01 31
 
0.6%
0.02 44
 
0.8%
0.03 26
 
0.5%
0.04 34
 
0.6%
0.05 23
 
0.4%
0.06 25
 
0.5%
0.07 27
 
0.5%
0.08 36
 
0.7%
0.09 36
 
0.7%
ValueCountFrequency (%)
1.66 1
 
< 0.1%
1.23 1
 
< 0.1%
1 6
0.1%
0.99 1
 
< 0.1%
0.91 1
 
< 0.1%
0.88 1
 
< 0.1%
0.86 1
 
< 0.1%
0.82 2
 
< 0.1%
0.81 2
 
< 0.1%
0.8 1
 
< 0.1%

residual sugar
Real number (ℝ)

Distinct316
Distinct (%)5.9%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean5.0538389
Minimum0.6
Maximum65.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2024-05-01T02:25:57.182616image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.1
Q11.8
median2.7
Q37.5
95-th percentile14.4
Maximum65.8
Range65.2
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation4.5040055
Coefficient of variation (CV)0.89120479
Kurtosis6.9861944
Mean5.0538389
Median Absolute Deviation (MAD)1.5
Skewness1.7027343
Sum26921.8
Variance20.286065
MonotonicityNot monotonic
2024-05-01T02:25:57.306177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6 200
 
3.8%
2 200
 
3.8%
1.4 194
 
3.6%
1.8 193
 
3.6%
1.2 173
 
3.2%
2.2 158
 
3.0%
1.5 149
 
2.8%
1.7 149
 
2.8%
2.1 149
 
2.8%
1.9 149
 
2.8%
Other values (306) 3613
67.8%
ValueCountFrequency (%)
0.6 1
 
< 0.1%
0.7 7
 
0.1%
0.8 25
 
0.5%
0.9 36
 
0.7%
0.95 3
 
0.1%
1 77
1.4%
1.05 1
 
< 0.1%
1.1 126
2.4%
1.15 3
 
0.1%
1.2 173
3.2%
ValueCountFrequency (%)
65.8 1
< 0.1%
31.6 1
< 0.1%
26.05 1
< 0.1%
23.5 1
< 0.1%
22.6 1
< 0.1%
22 1
< 0.1%
20.8 2
< 0.1%
20.7 1
< 0.1%
20.4 1
< 0.1%
20.3 1
< 0.1%

chlorides
Real number (ℝ)

HIGH CORRELATION 

Distinct214
Distinct (%)4.0%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean0.056668294
Minimum0.009
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2024-05-01T02:25:57.849975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.028
Q10.038
median0.047
Q30.066
95-th percentile0.104
Maximum0.611
Range0.602
Interquartile range (IQR)0.028

Descriptive statistics

Standard deviation0.036844532
Coefficient of variation (CV)0.65017895
Kurtosis48.310343
Mean0.056668294
Median Absolute Deviation (MAD)0.011
Skewness5.3409655
Sum301.872
Variance0.0013575195
MonotonicityNot monotonic
2024-05-01T02:25:57.963078image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.036 166
 
3.1%
0.044 161
 
3.0%
0.042 158
 
3.0%
0.046 156
 
2.9%
0.04 152
 
2.9%
0.047 148
 
2.8%
0.048 143
 
2.7%
0.05 141
 
2.6%
0.038 141
 
2.6%
0.034 138
 
2.6%
Other values (204) 3823
71.7%
ValueCountFrequency (%)
0.009 1
 
< 0.1%
0.012 2
 
< 0.1%
0.013 1
 
< 0.1%
0.014 4
 
0.1%
0.015 3
 
0.1%
0.016 5
 
0.1%
0.017 5
 
0.1%
0.018 8
0.2%
0.019 7
0.1%
0.02 13
0.2%
ValueCountFrequency (%)
0.611 1
< 0.1%
0.61 1
< 0.1%
0.467 1
< 0.1%
0.464 1
< 0.1%
0.422 1
< 0.1%
0.415 2
< 0.1%
0.414 2
< 0.1%
0.413 1
< 0.1%
0.403 1
< 0.1%
0.401 1
< 0.1%

free sulfur dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct135
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.060143
Minimum1
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2024-05-01T02:25:58.084665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q116
median28
Q341
95-th percentile61
Maximum289
Range288
Interquartile range (IQR)25

Descriptive statistics

Standard deviation17.815588
Coefficient of variation (CV)0.59266478
Kurtosis9.473417
Mean30.060143
Median Absolute Deviation (MAD)12
Skewness1.3586396
Sum160190.5
Variance317.39517
MonotonicityNot monotonic
2024-05-01T02:25:58.196214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 150
 
2.8%
29 145
 
2.7%
26 134
 
2.5%
15 132
 
2.5%
24 128
 
2.4%
34 124
 
2.3%
17 124
 
2.3%
31 124
 
2.3%
23 121
 
2.3%
28 115
 
2.2%
Other values (125) 4032
75.7%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 2
 
< 0.1%
3 50
 
0.9%
4 43
 
0.8%
5 111
2.1%
5.5 1
 
< 0.1%
6 150
2.8%
7 82
1.5%
8 77
1.4%
9 81
1.5%
ValueCountFrequency (%)
289 1
< 0.1%
146.5 1
< 0.1%
138.5 1
< 0.1%
131 1
< 0.1%
128 1
< 0.1%
124 1
< 0.1%
122.5 1
< 0.1%
118.5 1
< 0.1%
112 1
< 0.1%
110 1
< 0.1%

total sulfur dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct276
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.18934
Minimum6
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2024-05-01T02:25:58.332761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19
Q175
median116
Q3154
95-th percentile206
Maximum440
Range434
Interquartile range (IQR)79

Descriptive statistics

Standard deviation56.781422
Coefficient of variation (CV)0.49725676
Kurtosis-0.30252555
Mean114.18934
Median Absolute Deviation (MAD)39
Skewness0.061696654
Sum608515
Variance3224.1299
MonotonicityNot monotonic
2024-05-01T02:25:58.448374image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111 54
 
1.0%
113 50
 
0.9%
114 49
 
0.9%
122 48
 
0.9%
98 48
 
0.9%
128 46
 
0.9%
117 44
 
0.8%
126 43
 
0.8%
101 43
 
0.8%
104 43
 
0.8%
Other values (266) 4861
91.2%
ValueCountFrequency (%)
6 2
 
< 0.1%
7 4
 
0.1%
8 11
 
0.2%
9 14
0.3%
10 24
0.5%
11 22
0.4%
12 26
0.5%
13 25
0.5%
14 30
0.6%
15 28
0.5%
ValueCountFrequency (%)
440 1
< 0.1%
366.5 1
< 0.1%
344 1
< 0.1%
313 1
< 0.1%
307.5 1
< 0.1%
303 1
< 0.1%
294 1
< 0.1%
289 1
< 0.1%
282 1
< 0.1%
278 1
< 0.1%

density
Real number (ℝ)

HIGH CORRELATION 

Distinct998
Distinct (%)18.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.99453588
Minimum0.98711
Maximum1.03898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2024-05-01T02:25:58.560986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.98711
5-th percentile0.989844
Q10.9922
median0.99464
Q30.99677
95-th percentile0.999172
Maximum1.03898
Range0.05187
Interquartile range (IQR)0.00457

Descriptive statistics

Standard deviation0.0029656294
Coefficient of variation (CV)0.002981923
Kurtosis8.693196
Mean0.99453588
Median Absolute Deviation (MAD)0.00225
Skewness0.66475079
Sum5299.8817
Variance8.7949576 × 10-6
MonotonicityNot monotonic
2024-05-01T02:25:58.679041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.992 60
 
1.1%
0.9972 59
 
1.1%
0.9928 54
 
1.0%
0.998 53
 
1.0%
0.9976 52
 
1.0%
0.9968 51
 
1.0%
0.9934 51
 
1.0%
0.9932 50
 
0.9%
0.9962 49
 
0.9%
0.9966 48
 
0.9%
Other values (988) 4802
90.1%
ValueCountFrequency (%)
0.98711 1
< 0.1%
0.98713 1
< 0.1%
0.98722 1
< 0.1%
0.9874 1
< 0.1%
0.98742 2
< 0.1%
0.98746 2
< 0.1%
0.98758 1
< 0.1%
0.98774 1
< 0.1%
0.98779 1
< 0.1%
0.98794 1
< 0.1%
ValueCountFrequency (%)
1.03898 1
< 0.1%
1.0103 1
< 0.1%
1.00369 1
< 0.1%
1.0032 1
< 0.1%
1.00315 2
< 0.1%
1.00295 1
< 0.1%
1.00289 1
< 0.1%
1.0026 2
< 0.1%
1.00242 1
< 0.1%
1.00241 1
< 0.1%

pH
Real number (ℝ)

Distinct108
Distinct (%)2.0%
Missing9
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean3.2244342
Minimum2.72
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2024-05-01T02:25:58.800360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.72
5-th percentile2.98
Q13.11
median3.21
Q33.33
95-th percentile3.5
Maximum4.01
Range1.29
Interquartile range (IQR)0.22

Descriptive statistics

Standard deviation0.16027501
Coefficient of variation (CV)0.049706399
Kurtosis0.43632193
Mean3.2244342
Median Absolute Deviation (MAD)0.11
Skewness0.3912152
Sum17153.99
Variance0.02568808
MonotonicityNot monotonic
2024-05-01T02:25:58.916058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.16 156
 
2.9%
3.22 154
 
2.9%
3.14 146
 
2.7%
3.15 144
 
2.7%
3.2 142
 
2.7%
3.24 141
 
2.6%
3.18 140
 
2.6%
3.19 136
 
2.6%
3.12 130
 
2.4%
3.17 128
 
2.4%
Other values (98) 3903
73.2%
ValueCountFrequency (%)
2.72 1
 
< 0.1%
2.74 2
 
< 0.1%
2.77 1
 
< 0.1%
2.79 2
 
< 0.1%
2.8 3
 
0.1%
2.82 1
 
< 0.1%
2.83 3
 
0.1%
2.84 1
 
< 0.1%
2.85 6
0.1%
2.86 8
0.2%
ValueCountFrequency (%)
4.01 2
< 0.1%
3.9 2
< 0.1%
3.85 1
< 0.1%
3.82 1
< 0.1%
3.81 1
< 0.1%
3.8 2
< 0.1%
3.79 1
< 0.1%
3.78 2
< 0.1%
3.77 2
< 0.1%
3.76 2
< 0.1%

sulphates
Real number (ℝ)

Distinct111
Distinct (%)2.1%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.53326761
Minimum0.22
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2024-05-01T02:25:59.052571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.35
Q10.43
median0.51
Q30.6
95-th percentile0.79
Maximum2
Range1.78
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.14972398
Coefficient of variation (CV)0.28076706
Kurtosis8.6128592
Mean0.53326761
Median Absolute Deviation (MAD)0.08
Skewness1.8092083
Sum2839.65
Variance0.02241727
MonotonicityNot monotonic
2024-05-01T02:25:59.183700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 212
 
4.0%
0.46 196
 
3.7%
0.54 193
 
3.6%
0.44 184
 
3.5%
0.48 170
 
3.2%
0.38 165
 
3.1%
0.52 162
 
3.0%
0.47 161
 
3.0%
0.49 159
 
3.0%
0.45 157
 
2.9%
Other values (101) 3566
66.9%
ValueCountFrequency (%)
0.22 1
 
< 0.1%
0.23 1
 
< 0.1%
0.25 4
 
0.1%
0.26 3
 
0.1%
0.27 10
 
0.2%
0.28 12
 
0.2%
0.29 12
 
0.2%
0.3 24
0.5%
0.31 31
0.6%
0.32 45
0.8%
ValueCountFrequency (%)
2 1
 
< 0.1%
1.98 1
 
< 0.1%
1.95 1
 
< 0.1%
1.62 1
 
< 0.1%
1.61 1
 
< 0.1%
1.59 1
 
< 0.1%
1.56 1
 
< 0.1%
1.36 3
0.1%
1.34 1
 
< 0.1%
1.33 1
 
< 0.1%

alcohol
Real number (ℝ)

HIGH CORRELATION 

Distinct111
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.548539
Minimum8
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2024-05-01T02:25:59.306332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile9
Q19.5
median10.4
Q311.4
95-th percentile12.7
Maximum14.9
Range6.9
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation1.1861336
Coefficient of variation (CV)0.1124453
Kurtosis-0.53820574
Mean10.548539
Median Absolute Deviation (MAD)0.9
Skewness0.54614548
Sum56213.163
Variance1.4069129
MonotonicityNot monotonic
2024-05-01T02:25:59.420420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 289
 
5.4%
9.4 261
 
4.9%
9.2 205
 
3.8%
10 204
 
3.8%
10.5 193
 
3.6%
11 176
 
3.3%
9.8 174
 
3.3%
9.3 169
 
3.2%
10.4 166
 
3.1%
10.2 158
 
3.0%
Other values (101) 3334
62.6%
ValueCountFrequency (%)
8 2
 
< 0.1%
8.4 4
 
0.1%
8.5 10
 
0.2%
8.6 16
 
0.3%
8.7 49
 
0.9%
8.8 67
1.3%
8.9 58
1.1%
9 138
2.6%
9.05 1
 
< 0.1%
9.1 127
2.4%
ValueCountFrequency (%)
14.9 1
 
< 0.1%
14.2 1
 
< 0.1%
14.05 1
 
< 0.1%
14 11
0.2%
13.9 3
 
0.1%
13.8 2
 
< 0.1%
13.7 5
0.1%
13.6 11
0.2%
13.56666667 1
 
< 0.1%
13.55 1
 
< 0.1%

quality
Real number (ℝ)

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7960218
Minimum3
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.8 KiB
2024-05-01T02:25:59.516345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum9
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.87992241
Coefficient of variation (CV)0.15181489
Kurtosis0.29847656
Mean5.7960218
Median Absolute Deviation (MAD)1
Skewness0.14899195
Sum30887
Variance0.77426345
MonotonicityNot monotonic
2024-05-01T02:25:59.595922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 2327
43.7%
5 1755
32.9%
7 857
 
16.1%
4 206
 
3.9%
8 149
 
2.8%
3 30
 
0.6%
9 5
 
0.1%
ValueCountFrequency (%)
3 30
 
0.6%
4 206
 
3.9%
5 1755
32.9%
6 2327
43.7%
7 857
 
16.1%
8 149
 
2.8%
9 5
 
0.1%
ValueCountFrequency (%)
9 5
 
0.1%
8 149
 
2.8%
7 857
 
16.1%
6 2327
43.7%
5 1755
32.9%
4 206
 
3.9%
3 30
 
0.6%

Interactions

2024-05-01T02:25:54.263817image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:38.490708image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:39.998577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:41.610845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:42.946927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:44.327540image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:45.679520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:47.032992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:48.742577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:50.128765image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:51.468288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:52.861608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:54.380354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:38.631340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:40.108169image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:41.725373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:43.089479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:44.454233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:45.796083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:47.173050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:48.851838image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:50.252896image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:51.579783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:52.981147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:54.494237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:38.775407image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:40.225749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:41.847418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:43.208034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:44.571823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:45.909674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:47.305580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:48.968345image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:50.367470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:51.699341image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:53.103715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:54.605105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:38.879985image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:40.336582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:41.952022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:43.320389image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:44.680064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:46.020221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:47.439642image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:49.078931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:50.475262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:51.815948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:53.218344image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:54.714453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:38.994446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:40.690488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:42.056579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:43.436000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:44.787652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:46.131062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:47.550366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:49.186135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:50.587848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:51.927006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:53.344361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:54.825068image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:39.193517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:40.807051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:42.166131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:43.554080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:44.897006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:46.253174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:47.660986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:49.294674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:50.701274image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:52.038553image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:53.458108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:54.931600image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:39.323048image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:40.921841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:42.272674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:43.670136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:45.006607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:46.365302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:47.779556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:49.420124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:50.809799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:52.152165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:53.566606image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:55.034128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:39.430295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:41.033021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:42.376773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:43.774312image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:45.111692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:46.471931image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:47.877146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:49.558865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:50.912357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:52.259254image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:53.681239image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:55.141747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:39.539777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:41.154108image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:42.481392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:43.885897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:45.220230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:46.581454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:47.979718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:49.670443image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:51.016918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:52.369472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:53.802572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:55.250280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:39.651313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:41.273652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:42.602913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:43.993974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:45.332008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:46.689082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:48.091774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:49.785008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:51.124442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:52.477046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:53.910183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:55.367442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:39.763880image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:41.390527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:42.714255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:44.106540image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:45.455147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:46.804384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:48.220357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:49.900615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:51.235997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:52.600564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:54.034730image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:55.539720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:39.883037image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:41.501287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:42.826851image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:44.216004image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:45.572723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:46.916862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:48.632950image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:50.014158image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:51.361145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:52.737535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2024-05-01T02:25:54.152773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2024-05-01T02:25:59.685000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
alcoholchloridescitric aciddensityfixed acidityfree sulfur dioxidepHqualityresidual sugarsulphatestotal sulfur dioxidetypevolatile acidity
alcohol1.000-0.4160.024-0.684-0.114-0.1670.1100.480-0.268-0.018-0.2820.132-0.061
chlorides-0.4161.000-0.0630.6080.360-0.2610.161-0.304-0.0330.376-0.2780.7520.432
citric acid0.024-0.0631.0000.0630.2780.119-0.3040.1170.0740.0330.1570.422-0.303
density-0.6840.6080.0631.0000.450-0.0240.040-0.3490.4940.3020.0230.3530.309
fixed acidity-0.1140.3600.2780.4501.000-0.267-0.261-0.105-0.0250.232-0.2470.5030.207
free sulfur dioxide-0.167-0.2610.119-0.024-0.2671.000-0.1610.0900.364-0.2360.7420.412-0.371
pH0.1100.161-0.3040.040-0.261-0.1611.0000.053-0.1950.233-0.2290.3140.178
quality0.480-0.3040.117-0.349-0.1050.0900.0531.000-0.0290.035-0.0580.127-0.251
residual sugar-0.268-0.0330.0740.494-0.0250.364-0.195-0.0291.000-0.1180.4300.328-0.023
sulphates-0.0180.3760.0330.3020.232-0.2360.2330.035-0.1181.000-0.2630.4720.262
total sulfur dioxide-0.282-0.2780.1570.023-0.2470.742-0.229-0.0580.430-0.2631.0000.794-0.342
type0.1320.7520.4220.3530.5030.4120.3140.1270.3280.4720.7941.000-0.601
volatile acidity-0.0610.432-0.3030.3090.207-0.3710.178-0.251-0.0230.262-0.342-0.6011.000

Missing values

2024-05-01T02:25:55.714000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-01T02:25:55.923632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-01T02:25:56.089775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
0white7.00.270.3620.700.04545.0170.01.00103.000.458.86
1white6.30.300.341.600.04914.0132.00.99403.300.499.56
2white8.10.280.406.900.05030.097.00.99513.260.4410.16
3white7.20.230.328.500.05847.0186.00.99563.190.409.96
4white6.20.320.167.000.04530.0136.00.99493.180.479.66
5white8.10.220.431.500.04428.0129.00.99383.220.4511.06
6white8.10.270.411.450.03311.063.00.99082.990.5612.05
7white8.60.230.404.200.03517.0109.00.99473.140.539.75
8white7.90.180.371.200.04016.075.00.99203.180.6310.85
9white6.60.160.401.500.04448.0143.00.99123.540.5212.47
typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
5319red7.2NaN0.332.50.06834.0102.00.994143.270.7812.86
5320red6.60.7250.207.80.07329.079.00.997703.290.549.25
5321red6.30.5500.151.80.07726.035.00.993143.320.8211.66
5322red5.40.7400.091.70.08916.026.00.994023.670.5611.66
5323red6.30.5100.132.30.07629.040.00.995743.420.7511.06
5324red6.80.6200.081.90.06828.038.00.996513.420.829.56
5325red6.20.6000.082.00.09032.044.00.994903.450.5810.55
5326red5.90.5500.102.20.06239.051.00.995123.52NaN11.26
5327red5.90.6450.122.00.07532.044.00.995473.570.7110.25
5328red6.00.3100.473.60.06718.042.00.995493.390.6611.06